Designing Adoption: An Intervention-Augmented UTAUT for AI Use Across Generations

Abstract

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Artificial intelligence (AI) adoption is often hindered more by human factors than technical barriers. End-users frequently lack confidence in integrating AI tools, and adoption intentions vary across demographic groups. This study introduces an intervention-augmented Unified Theory of Acceptance and Use of Technology (UTAUT) model to explain and enhance $\mathbf{A I}$ adoption across generations. The study utilized the explanatory sequential mixed-methods design. A survey of $\mathbf{1, 0 3 8}$ respondents confirmed two adoption pathways: (1) performance expectancy influenced the intention to adopt AI through performance expectancy; and (2) performance expectancy influenced intention to adopt AI through social influence and facilitating conditions. Building on these findings, stakeholders representing academics, relevant government agencies, and industry experts co-created a national AI training program. Post-training results demonstrated an increase in AI adoption intention eliminating the generational difference in adoption. The study demonstrates stakeholder-co-created intervention as an antecedent in UTAUT and offers practical guidance for human-computer interaction and policy strategies to promote inclusive AI adoption.